Image processing apparatus, monitoring system, image processing method,and program

ABSTRACT

Provided is an image processing apparatus ( 2000 ) including an index value calculation unit ( 2020 ) and a presentation unit ( 2040 ). The index value calculation unit ( 2020 ) acquires a plurality of images captured by a camera ( 3000 ) (captured images), and calculates an index value indicating the degree of change in the state of a monitoring target in the captured image, using the acquired captured image. The presentation unit ( 2040 ) presents an indication based on the index value calculated by the index value calculation unit ( 2020 ) on the captured image captured by the camera ( 3000 ).

TECHNICAL FIELD

The present invention relates to an image processing technique.

BACKGROUND ART

A method of monitoring a facility or the like includes a method ofperforming monitoring by viewing images obtained from a monitoringcamera that captures images of the facility or the like. A technique forfacilitating monitoring using a monitoring camera has been developed.

Patent Document 1 discloses an abnormal behavior detection apparatusthat detects abnormal behaviors. This apparatus divides the level ofcongestion into a plurality of stages and obtains a normal movementpattern on the basis of the level of congestion. The determination ofwhether being abnormal behavior is performed by determining whether ornot a movement pattern of a target object matches the normal movementpattern based on the level of congestion at that time.

Patent Document 2 discloses a monitoring system having a function ofpresenting the state of a monitoring target on an image which isdisplayed on a monitor. Specifically, the degree of commonness of amoving direction of a crowd and a numerical value indicating the movingdirection of the crowd are presented on an image obtained by capturingan image of the crowd.

RELATED DOCUMENT Patent Document

[Patent Document 1] Japanese Unexamined Patent Application PublicationNo. 2010-072782

[Patent Document 2] Japanese Unexamined Patent Application PublicationNo. 2012-022370

SUMMARY OF THE INVENTION

In the techniques disclosed in the related art, it may be difficult toimmediately ascertain the current condition of a monitoring target. Forexample, when an observer desires to ascertain whether a person capturedby a monitoring camera is a person passing by the place or is a personprowling about the place, the observer needs to continue viewing animage captured by the monitoring camera for a certain period of time.

The invention is contrived in view of the above-mentioned problem, andan object thereof is to provide a technique with which a personmonitoring a monitoring camera can immediately ascertain the currentcondition of a monitoring target.

There is provided an image processing apparatus including an index valuecalculation unit calculating an index value indicating a degree ofchange in a state of a monitoring target in a plurality of capturedimages using the captured images, the captured images being captured bya camera at different times; and a presentation unit presenting anindication based on the index value on a first captured image capturedby the camera.

There is provided a monitoring system including a camera, an imageprocessing apparatus, and a display screen.

The image processing apparatus is the above-described image processingapparatus of the invention. In addition, the display screen displays thefirst captured image on which an indication based on the index value ispresented by the presentation unit.

There is provided an image processing method performed by a computer.The method includes calculating an index value indicating a degree ofchange in a state of a monitoring target in a plurality of capturedimages using the captured images, the captured images being captured bya camera at different times; and presenting an indication based on theindex value on a first captured image captured by the camera.

There is provided a program that causes a computer to have a function ofoperating as the image processing apparatus of the invention by causingthe computer to have functions of functional components included in theimage processing apparatus of the invention.

According to the invention, provided is a technique with which a personmonitoring a monitoring camera can immediately ascertain the currentcondition of a monitoring target.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects described above, and other objects, features and advantagesare further made more apparent by suitable embodiments that will bedescribed below and the following accompanying drawings.

FIG. 1 is a block diagram illustrating an image processing apparatusaccording to a first exemplary embodiment.

FIG. 2 is a diagram conceptually illustrating a process of calculatingan index value of a monitoring target for each presentation targetimage.

FIG. 3 is a diagram conceptually illustrating a process of presenting anindication which is common to a plurality of presentation target images.

FIG. 4 is a block diagram illustrating a hardware configuration of theimage processing apparatus.

FIG. 5 is a flow chart illustrating a flow of processing performed bythe image processing apparatus of the first exemplary embodiment.

FIGS. 6A and 6B are diagrams conceptually illustrating a process ofpresenting an indication based on an index value in the format of ananimation.

FIGS. 7A and 7B are diagrams illustrating a state where people are leftbehind.

FIG. 8 is a block diagram illustrating an image processing apparatusaccording to a second exemplary embodiment.

FIG. 9 is a diagram illustrating a color map in which a black becomesdarker on the basis of an index value.

FIG. 10 is a diagram illustrating a rainbow-colored color map.

FIGS. 11A and 11B are diagrams conceptually illustrating that colors ofa monitoring target and its surroundings are changed into thosecorresponding to an index value indicating the degree of change in theposition of the monitoring target.

FIGS. 12A and 12B are diagrams conceptually illustrating thatemphasizing is performed by presenting a frame around a monitoringtarget.

FIGS. 13A and 13B are diagrams conceptually illustrating thatemphasizing is performed by presenting a frame having a color and widthcorresponding to an index value around a monitoring target.

FIG. 14 is a block diagram illustrating an image processing apparatusaccording to a fourth exemplary embodiment.

FIG. 15 is a diagram conceptually illustrating a method of determiningthe density of an indication color on the basis of the degree ofdivergence when a reference density is determined.

FIG. 16 is a diagram conceptually illustrating that images captured by aplurality of cameras are displayed on a display screen in atime-division manner.

FIG. 17 is a diagram illustrating a method for an index valuecalculation unit to calculate an index value, according to a sixthexemplary embodiment.

FIG. 18 is a flow chart illustrating a flow of processing performed byan image processing apparatus according to the sixth exemplaryembodiment.

FIG. 19 is a diagram illustrating a relationship between a user's eyegaze direction and a partial region.

FIG. 20 is a diagram illustrating information in which a partial regioncorresponding to an observer's eye gaze direction and time at which theeye gaze direction of the observer has changed, in a table format.

DESCRIPTION OF EMBODIMENTS

Hereinafter, exemplary embodiments of the invention will be describedwith reference to the accompanying drawings. In all the drawings, likereference numerals denote like components, and descriptions thereof willnot be repeated.

First Exemplary Embodiment

FIG. 1 is a block diagram illustrating an image processing apparatus2000 according to a first exemplary embodiment. In FIG. 1, an arrowindicates a flow of information. Further, in FIG. 1, each blockindicates a function-based configuration instead of a hardware-basedconfiguration.

The image processing apparatus 2000 includes an index value calculationunit 2020 and a presentation unit 2040. The index value calculation unit2020 acquires a plurality of images which are captured by a camera 3000(hereinafter, captured images). An example of the camera 3000 is amonitoring camera. In addition, the plurality of captured images arecaptured at different times. For example, the plurality of capturedimages are frames constituting a movie which the camera 3000 captures.

Further, the index value calculation unit 2020 calculates an index valueindicating the degree of change in the state of a monitoring target inan acquired captured image using the captured images.

The presentation unit 2040 presents an indication, which is based on theindex value calculated by the index value calculation unit 2020, on theimage that the camera 3000 captures. Here, the captured image may be animage used for the calculation of an index value, or may be an image notused for the calculation of an index value. In the former case, forexample, the presentation unit 2040 presents an indication based on anindex value calculated using first to n-th captured images on the n-thcaptured image. In addition, in the latter case, for example, thepresentation unit 2040 presents an indication based on an index valuecalculated using the first to n-th captured images on an (n+1)-thcaptured image. Hereinafter, a captured image of a target on which thepresentation unit 2040 presents an indication based on an index value isalso written as a presentation target image.

For example, the presentation unit 2040 calculates an index value of amonitoring target for each presentation target image. FIG. 2 is adiagram conceptually illustrating a process of calculating an indexvalue of a monitoring target for each presentation target image. In FIG.2, the presentation unit 2040 presents an indication based on an indexvalue calculated using the first to n-th captured images on an (n+1)-thcaptured image. Similarly, the presentation unit 2040 presents anindication based on an index value calculated using the second to(n+1)-th captured images on an (n+2)-th captured image, and presents anindication based on an index value calculated using the third to(n+2)-th captured images on an (n+3)-th captured image.

In addition, for example, the presentation unit 2040 may use an indexvalue calculated using a plurality of captured images in common for aplurality of presentation target images. FIG. 3 is a diagramconceptually illustrating a process of presenting an indication which iscommon to a plurality of presentation target images. In FIG. 3, thepresentation unit 2040 presents an indication based on an index valuecalculated using first to n-th captured images on each of (n+1)-th to2n-th captured images. Similarly, the presentation unit 2040 presents anindication based on an index value calculated using the (n+1)-th to2n-th captured images on each of (2n+1)-th to 3n-th captured images.

Example of Hardware Configuration

Each functional component of the image processing apparatus 2000 may beimplemented with a hardware constituent element (for example, anhard-wired electronic circuit or the like) which implements eachfunctional component, or may be implemented by a combination of ahardware constituent element and a hardware constituent element (forexample, a combination of an electronic circuit and a program forcontrolling the electronic circuit, or the like).

FIG. 4 is a block diagram illustrating a hardware configuration of theimage processing apparatus 2000. The image processing apparatus 2000includes a bus 1020, a processor 1040, a memory 1060, a storage 1080,and an input-output interface 1100. The bus 1020 is a data transmissionchannel for allowing the processor 1040, the memory 1060, the storage1080, and the input-output interface 1100 to transmit and receive datato and from each other. Here, a method of connecting the processor 1040and the like to each other is not limited to bus connection. Theprocessor 1040 is an arithmetic processing device such as, for example,a central processing unit (CPU) or a graphics processing unit (GPU). Thememory 1060 is a memory such as, for example, a random access memory(RAM) or a read only memory (ROM). The storage 1080 is a storage devicesuch as, for example, a hard disk, a solid state drive (SSD), or amemory card. In addition, the storage 1080 may be a memory such as a RAMor a ROM. The input-output interface 1100 is an input-output interfacefor allowing the image processing apparatus 2000 to transmit and receivedata to and from an external apparatus and the like. For example, theimage processing apparatus 2000 acquires a captured image through theinput-output interface 1100. In addition, for example, the imageprocessing apparatus 2000 outputs a captured image on which anindication based on an index value is presented, through theinput-output interface 1100.

The storage 1080 includes an index value calculation module 1220 and apresentation module 1240 as a program for realizing the function of theimage processing apparatus 2000. The processor 1040 realizes thefunctions of the index value calculation unit 2020 and the presentationunit 2040 by executing the modules. Here, when the above-mentionedmodules are executed, the processor 1040 may execute the modules afterreading the modules on the memory 1060 or may execute the moduleswithout reading the modules on the memory 1060.

The hardware configuration of the image processing apparatus 2000 is notlimited to the configuration illustrated in FIG. 4. For example, eachmodule may be stored in the memory 1060. In this case, the imageprocessing apparatus 2000 may not include the storage 1080.

<Flow of Processing>

FIG. 5 is a flow chart illustrating a flow of processing that the imageprocessing apparatus 2000 of the first exemplary embodiment performs. Instep S102, the index value calculation unit 2020 acquires a capturedimage. In step S104, the presentation unit 2040 calculates an indexvalue indicating the degree of change in the state of a monitoringtarget in the captured image. In step S106, the presentation unit 2040presents an indication based on an index value on an image (presentationtarget image) that the camera 3000 captures.

<Operational Advantages>

When an observer or the like wants to ascertain to what extent the stateof a monitoring target captured by a monitoring camera has changed, theobserver needs to continuously view the object of the monitoring camera.Even when the state of the monitoring target at that time can beascertained only by viewing the image for a short period of time, forexample, about one second, it is difficult to ascertain to what extentthe state of the monitoring target has changed.

On the other hand, according to the image processing apparatus 2000 ofthe present exemplary embodiment, an indication indicating the degree ofchange in the state of a monitoring target is presented on apresentation target image. Suppose that an image captured by the camera3000 is displayed on a display screen 4000. In this case, the displayscreen 4000 displays an image on which an indication based on an indexvalue is overlapped. For this reason, an observer or the like can easilyascertain in a short period of time to what extent the state of amonitoring target has changed. Accordingly, the observer or the like canimmediately and easily ascertain the current condition of the monitoringtarget.

Hereinafter, the present exemplary embodiment will be described in moredetail.

<Method of Acquiring Captured Image>

A method for the index value calculation unit 2020 to acquire a capturedimage is arbitrary. For example, the index value calculation unit 2020acquires a captured image from the camera 3000. In addition, the indexvalue calculation unit 2020 may acquire a captured image stored in astorage device which is located outside the camera 3000. In this case,the camera 3000 is configured to store a captured image in the storagedevice. The storage device may be provided within the image processingapparatus 2000, or may be provided outside the image processingapparatus 2000.

In addition, a process of acquiring a captured image may be a process inwhich the index value calculation unit 2020 receives a captured imagewhich the camera 3000 or the above-mentioned storage device outputs, ormay be a process in which the index value calculation unit 2020 readsout a captured image from the camera 3000 or the above-mentioned storagedevice.

<Details of Monitoring Target>

There are various monitoring targets that the image processing apparatus2000 handles. For example, the image processing apparatus 2000 handlesan object (a person, a thing or the like) or a set of objects (crowd orthe like) as monitoring targets. Note that, an object indicating a thingmay include a place. In other words, the image processing apparatus 2000may handles a place (region) in a captured image as a monitoring target.

For example, the index value calculation unit 2020 divides a regionincluded in a captured image into a foreground region and a backgroundregion, and handles the foreground region as an object. Here, a methodof extracting an object such as a person or a thing from an image is notlimited to the above-described method. Techniques of extracting objectssuch as a person and a thing from an image are already known, and theindex value calculation unit 2020 can use the known techniques. Here,the known techniques will not be described.

<Method of Determining Monitoring Target>

The image processing apparatus 2000 may set all objects extracted from acaptured image as monitoring targets, or may set only specific objectsas monitoring targets. For example, the image processing apparatus 2000handles only a person or a set of people (crowd) as a monitoring target.In addition, the image processing apparatus 2000 may set only a specificperson or crowd as a monitoring target. In this case, the imageprocessing apparatus 2000 acquires information indicating a monitoringtarget (for example, a blacklist), and determines the monitoring targeton the basis of the information. The information indicating a monitoringtarget indicates, for example, a feature value of each monitoringtarget. In addition, the information indicating a monitoring target maybe information indicating the features of a person to be monitored, suchas “wearing a hat” or “wearing sunglasses”. Here, since a technique ofdetermining an object having a specific feature from the objectsincluded in an image is a known technique, a detailed method will not bedescribed.

<Details of Presentation Unit 2040>

As described above, the presentation unit 2040 presents an indicationbased on an index value, on an image captured by the camera 3000(presentation target image). For example, a process of presenting anindication based on an index value on a presentation target image is aprocess of presenting an index value calculated for a monitoring targetnear the monitoring target in the presentation target image. Otherexamples of the “process of presenting an indication based on an indexvalue on a presentation target image” will be described in exemplaryembodiments later, and the like.

Here, the phrase “presenting an indication on a presentation targetimage” refers to, for example, a process of combining the indicationinto the presentation target image or overlapping the indication on thepresentation target image. In this case, the presentation unit 2040 mayoutput the presentation target image having the indication combinedthereinto to an output device such as the display screen 4000 or thelike, or may store the presentation target image in a storage deviceprovided inside or outside the image processing apparatus 2000. In thelatter case, the display screen 4000 or another device reads thepresentation target image stored in the storage device and outputs theimage to the display screen 4000. Note that, the display screen 4000 is,for example, a monitor installed in a workroom or the like of anobserver, a monitor of a mobile phone of a security guard observing inthe scene, or the like.

In addition, the presentation unit 2040 may separately generate imagedata indicating an indication based on an index value without combiningthe indication into a presentation target image. In this case, theindication is presented on the presentation target image by displayingthe image data together with presentation target data.

In addition, the presentation unit 2040 may present an indication basedon an index value on a map by using map data of a facility in which thecamera 3000 is installed. The map data is displayed on the displayscreen 4000 or a monitor of a security guard's mobile phone or the like.The position of a monitoring target on the map can be calculated on thebasis of various parameters of the camera 3000 (the position, theorientation or the like of the installed camera) and the position of themonitoring target on a captured image. In this case, the presentationunit 2040 acquires and uses map data of the facility in which the camera3000 is installed and various parameters related to the camera 3000.Note that, a relationship between the various parameters of the camera3000 and the position of the camera on the map is defined in advance byperforming a process such as calibration.

In addition, the presentation unit 2040 may present an indication basedon an index value calculated for a monitoring target in the format of ananimation (frame-by-frame playback). FIGS. 6A and 6B are diagramconceptually illustrating a process of presenting an indication based onan index value in the format of an animation. In FIG. 6A, the indexvalue calculation unit 2020 calculates an index value indicating thedegree of change in the states of monitoring targets in first to n-thcaptured images, and generates an indication 1 on the basis of the indexvalue. Similarly, the index value calculation unit 2020 generates anindication 2 using (n+1)-th to 2n-th captured images, and generates anindication 3 using (2n+1)-th to 3n-th captured images.

In FIG. 6B, the presentation unit 2040 presents the indication 1 on the3n-th captured image, presents the indication 2 on a (3n+1)-th capturedimage, and presents the indication 3 on a (3n+2)-th captured image.

By doing so, the indications 1 to 3 are presented in the format of ananimation. Further, the presentation unit 2040 may also repeat displayssuch as “display 1, display 2, and display 3” for the subsequentcaptured images. In this manner, an animation constituted by theindication 1 to the indication 3 is repeatedly presented on a capturedimage.

<Method of Calculating Index Value>

As described above, the index value calculation unit 2020 calculates anindex value indicating the degree of change in the state of a monitoringtarget in a captured image. Here, there are various “states of amonitoring target” which the image processing apparatus 2000 handles asmonitoring targets, and the method of calculating an index value dependson what is handled as a monitoring target. Consequently, hereinafter, astate of a monitoring target handled by the image processing apparatus2000 and a method of calculating an index value indicating the degree ofchange in the state of the monitoring target will be described.

<<Position of Monitoring Target>>

For example, the index value calculation unit 2020 handles the positionof a monitoring target as the state of the monitoring target. Forexample, when there is a person standing for a long period of time at apath where people pass through, it is considered that the person shouldbe attentively monitored. Thus, the index value calculation unit 2020handles the degree of change in the position of a monitoring target, asthe degree of change in the state of the monitoring target. The degreeof change in the position of the monitoring target can be described asthe degree of staying of the monitoring target in another way. Anobserver or the like can immediately ascertain the degree of staying ofeach monitoring target by calculating an index value on the basis of thedegree of staying of the monitoring target and presenting an indicationbased on the index value on a presentation target captured image.

For example, the degree of change in the position of a monitoring targetis represented by the length of time for which a certain monitoringtarget (the same person, a crowd, or the like) is seen in a capturedimage. Here, the length of time for which the monitoring target in thecaptured image can be represented, for example, according to how manycaptured images show the monitoring target, among captured images whichare captured in time series (frames constituting a movie).

In addition, for example, the index value calculation unit 2020 mayrepresent the degree of change in the position of a monitoring target,by the size of a moving range of the monitoring target. For example, thesize of the moving range of the monitoring target is represented by thesize of a region (a circular shape, a rectangular shape, or the like)which includes all of the positions of monitoring targets in eachcaptured image. Here, the size of the region is represented by the areaof the region or the length of the side or the diameter of the region.

Further, the index value calculation unit 2020 may calculate the degreeof change in the position of a monitoring target, also in considerationof the degree of spatial movement such as the movement of a portion ofthe body of the monitoring target.

<<Frequency at which Monitoring Target is Captured in Captured Images>>

In addition, for example, the index value calculation unit 2020 handlesthe frequency at which a certain monitoring target is seen in a capturedimage, as a state of the monitoring target. In other words, the indexvalue calculation unit 2020 handles the degree of change in thefrequency at which a certain monitoring target is seen in a capturedimage (the length of time for which the object being seen in thecaptured image) as the degree of change in the state of the monitoringtarget. Suppose that a certain monitoring target is not detected in acaptured image over the first thirty minutes, is detected once in thecaptured image over the next thirty minutes, and is detected five timesin the captured image over another subsequent period of thirty minutes.In this case, the frequency at which the monitoring target in thecaptured image is increasing. For this reason, the degree of change inthe state of the monitoring target is high.

For example, in this case, since the frequency at which the monitoringtarget in the place gradually increased, it is also considered that theobject is behaving unnaturally. For example, it is possible thathabitual prowling or previewing of the scene before committing a crimemay be performed. For this reason, it is preferable that an observer orthe like performing monitoring by viewing a captured image attentivelymonitors such a monitoring target. Thus, the image processing apparatus2000 calculates an index value on the basis of the degree of change inthe frequency at which a monitoring target in a captured image, andpresents an indication based on the index value on a presentation targetimage. Thereby, an observer or the like viewing the presentation targetimage can immediately ascertain the degree of change in the frequency atwhich each monitoring target is shown in a captured image.

For example, the index value calculation unit 2020 counts the number oftimes that each monitoring target is detected in a captured image foreach predetermined period of time. The index value calculation unitcalculates the degree of change in the frequency at which the monitoringtarget is detected in the captured image using the number of detectionsof the monitoring target, which number is calculated for eachpredetermined period of time. Alternatively, a time interval between thedetections may be obtained, and the degree of change in the length ofthe time interval between the detections may be calculated.

<<Degree of Crowdedness of Monitoring Target>>

For example, the index value calculation unit 2020 handles the degree ofcrowdedness of a monitoring target as the state of the monitoringtarget. For example, when people are handled as monitoring targets, thedegree of crowdedness of the monitoring targets is how much the peoplecrowds, and is also described as the degree of congestion in anotherway. For example, when a narrow path is overcrowded with people, thereis a risk of a crowd surge. In this case, since an action, such as anappropriate guidance by security guard's, is required, it is preferablethat an observer viewing an image provided by a monitoring camera canimmediately ascertain such a situation. The image processing apparatus2000 presents an indication based on the degree of change in the degreeof crowdedness of monitoring targets, on a presentation target image.Thus, the observer viewing the presentation target image can immediatelyrecognize monitoring targets the congestion of which is still noteliminated even after the elapse of time.

The degree of crowdedness of monitoring target can be represented using,for example, the size of the monitoring target and the number of objectsincluded in the monitoring target. Here, the size of the monitoringtarget is represented by the size of a region indicating the monitoringtarget. A method of representing the size of the region is as describedabove. For example, the index value calculation unit 2020 calculates thedegree of crowdedness of a monitoring target using Expression (1). InExpression (1), “d” denotes the degree of crowdedness, “n” denotes thenumber of objects included in the monitoring target, and “a” denotes anarea of a region indicating the monitoring target.

$\begin{matrix}\left\lbrack {{Expression}\mspace{14mu} 1} \right\rbrack & \; \\{d = \frac{n}{a}} & (1)\end{matrix}$

Here, n may be the number of objects calculated by individuallynumerating the objects, or may be the number of objects estimated bycollectively recognizing a group of the plurality of objects.

For example, the index value calculation unit 2020 calculates the degreeof change in the degree of crowdedness by calculating theabove-mentioned degree of crowdedness for each predetermined period oftime.

<<Length or Speed of Queue of Monitoring Target>>

For example, the index value calculation unit 2020 handles the length ofa queue of a monitoring target or the speed of proceeding thereof as thestate of the monitoring target. Suppose that, in a store having aplurality of register counters, there is a queue of a register counterthe length of which does not change for a long period of time (the speedof proceeding of a queue is low) among queues of each register counter.In this case, it is considered that a certain trouble occurs at thatregister counter.

Thus, the index value calculation unit 2020 calculates an index value onthe basis of the degree of change in the length of the queue of themonitoring target or the speed of proceeding thereof. The length of thequeue of the monitoring target may be represented by the size of aregion indicating the queue, or may be represented by the number ofobjects included in the queue of the monitoring target. Here, supposethat the length of the side or diameter of the region represents “thesize of the region indicating the queue”. In this case, for example, theindex value calculation unit 2020 calculates the direction of the queuebased on a direction in which the length of the queue changes, theorientations of objects included in the queue, and the like, and the“length of the region indicating the queue” is represented using thelength of the side or diameter in the direction in the region indicatingthe queue.

Note that, the direction of the queue may be given in advance inassociation with the camera 3000. For example, when the orientation ofthe camera 3000 is fixed and the positions of the register counter andthe like are also fixed, it is possible to determine the orientation ofthe queue in advance.

In addition, the index value calculation unit 2020 calculates the speedof proceeding of the queue from the degree of change in the length ofthe queue.

Alternatively, it is also possible to calculate the speed of the queueby focusing on a specific object in the queue.

<<Degree of being Left Behind>>

For example, the index value calculation unit 2020 sets a person, abaggage, or the like in an image obtained by capturing a platform of astation or the like as monitoring targets. The index value calculationunit 2020 calculates the degree of change in the number of people,pieces of baggage, or the like or the degree of change in the length ofa queue of people or pieces of baggage (how much the people or thepieces of baggage are left behind), as the degree of change in the stateof the monitoring target.

FIGS. 7A and 7B are diagrams illustrating a state where people are leftbehind. FIG. 7A illustrates a captured image 10-1 obtained by capturinga state immediately after a door of a train opens, and FIG. 7Billustrates a captured image 10-2 obtained by capturing a stateimmediately before a door of a train closes. Comparing the two capturedimages with each other, many people do not get on the train and are leftin front of the door on the front side, whereas there is no one leftbehind at the door on the back side. When the degree of being leftbehind varies greatly depending on a boarding position as describedabove, it is considered that there are any troubles at the platform orwithin the train. The presentation unit 2040 presents an indicationbased on the degree of being left behind on a presentation target image,and thus an observer viewing the presentation target image canimmediately ascertain how much people, pieces of baggage, or the likeare left behind.

<<The Degree of Dissatisfaction of Monitoring Target>>

The index value calculation unit 2020 may determine not only the stateof a monitoring target (e.g. the above-mentioned position of themonitoring target) which is directly obtained by analyzing a capturedimage, but also the state of a monitoring target on the basis of anindex obtained by applying the state to a model or the like.

For example, the index value calculation unit 2020 handles the degree ofdissatisfaction of a monitoring target as a state of the monitoringtarget. Here, suppose that the monitoring target is a crowd. The indexvalue calculation unit 2020 calculates the degree of dissatisfaction ofthe crowd from the degree of congestion and information about the flowof the crowd. For example, it may be considered that a crowd having ahigh degree of congestion or a slow flow generally tends to becomeincreasingly dissatisfied.

Thus, the degree of dissatisfaction of the crowd is modeled on the basisof a function of F(u, v) using the degree of congestion “u” and a speed“v”. Here, for example, F(u, v) is of a monotone non-decreasing functionof “u” and a monotone non-increasing function of “v”. When theinfluences from “u” and “v” are independent on each other, it isdescribed as F(u, v)=f(u)g(v) with f(u) being set as a monotonenon-decreasing function of “u” and g(v) being set as a monotonenon-increasing function of “v”.

Note that, the degree of dissatisfaction increases when the speed of thecrowd is low, and the degree of dissatisfaction could also increase evenwhen the speed of the crowd is too high. This is because people in thecrowd feel the stress due to a difficulty in following the flow of thecrowd. Thus, g(v) may be modeled by a function which increases when “v”increases to a certain extent.

In addition, in a case of being lined up in queues, people would becomemore dissatisfied if the queue in which they are does not proceed whileother queues proceed. Thus, the speeds of the proceeding of therespective queues are compared with each other. When the speed ofproceeding of a certain queue is lower than the speeds of proceeding ofthe other queues, the degree of dissatisfaction may be increased so thatit is equal to or higher than the degree of dissatisfaction determinedwith the value of “v”. In other words, when Δv is set as a difference inthe speed between its own line and the neighboring line (the valueobtained by subtracting the speed of its own line from the speed of theneighboring line), the modeling may be performed using g2(v, Δv) whichis a monotone non-decreasing function with respect to Δv instead ofg(v). Here, it is assumed that Δv has a positive value when the speed ofits own queue is relatively low. And, it is assumed that the relation ofg2(v, 0)=g(v) is satisfied.

This method can also be applied to objects other than a crowdconstituting a queue. Suppose that a flow of a certain crowd becomesslower than surrounding flows due to the presence of an obstacle or apeople having walking handicap. In this case, the crowd may be modeledso that the degree of dissatisfaction increases. That is, when agradient of “v” is set to ∇v, the influence of the speed of the flow maybe modeled by g2(v, ∇v). In addition, the degree of dissatisfaction maybe calculated on the basis of the positions of people belonging to thesame crowd in a line (how much the people are far from the front) and anestimated time until the people reach the front of the line. This isbecause it is considered that a person closer to the front would finishan action of being in a line earlier, and thus would be more patientwith dissatisfaction.

Note that, the functions may vary due to other external factors. Otherexternal factors include temperature, humidity, weather, brightness, andthe like. For example, when the temperature is too high or two low ascompared to a case of an appropriate temperature, it may be consideredthat the degree of dissatisfaction tends to increase. Thus, a model maybe used in which the degree of dissatisfaction decreases under anappropriate temperature and increases under the temperature beingoutside the appropriate temperature. Similarly, it is considered thatthe degree of dissatisfaction tends to increase in a case of rain ascompared to a case of fine weather. Thus, a model may be used in whichthe degree of dissatisfaction tends to increase in a case of rain ascompared to a case of fine weather. In addition, when a facility forwhich monitoring is performed using the camera 3000 is a stadium inwhich a game is played or the like, the winning and losing of the gameand the like may be external factors. For example, when a person in acrowd is a supporter of a team that has lost or almost loses the game,modeling is performed so that the degree of dissatisfaction furtherincreases.

<<Degree of Risk of Monitoring Target>>

For example, the index value calculation unit 2020 calculates how severedamage may occur when any event occurs near a monitoring target (forexample, when a suspicious substance explodes or when a person with aweapon appears), that is, the degree of risk near the monitoring target,as the degree of risk of the monitoring target.

Specifically, since there would be many victims when an event occurs ina place very crowded with people, the degree of risk is high. Inaddition, even when the place is not crowded with people, the degree ofrisk is high in a place where a crowd gets panic and has difficulty inrunning away due to a characteristic of the structure of a building whenan accident occurs. Specifically, it may be the place having a highdegree of risk where the number of exits is small or the width of anexit is small with respect to the number of people capable of beingaccommodated in the place, or the exit is far therefrom.

Such a degree of risk is determined by the structural characteristics ofthe building and the state of the crowd. And, it is possible to generatea model for calculating the degree of risk by performing a simulationfor the behavior of the crowd in advance with respect to various statesof crowds. The index value calculation unit 2020 applies a feature valueof the state of a crowd (density or flow) in a certain place actuallyshown in a captured image to the above-mentioned model, and therebycalculating the degree of risk in the place. Note that, it is able todetermine a place where a crowd exists with the camera 3000 thatcaptures the captured image showing the crowd. For example, when thesight of the camera 3000 is fixed or changes in a narrow range, it isable to specify a place where a monitoring target shown in a certaincaptured image exists by using an ID and the like of the camera 3000having captured the captured image. In addition, when the camera 3000monitors a wide range while changing its orientation, it is able tospecify a position of a monitoring target shown in a certain capturedimage exists, for example, by using an ID and the like of the camera3000 and the orientation of the camera 3000 at the time of imagecapturing.

Note that, the index value calculation unit 2020 may calculate thedegree of risk in consideration of characteristics of a crowd to bemonitored. For example, the index value calculation unit 2020 uses amodel of calculating a high degree of risk for a crowd requiring timefor movement (for example, a crowd including a group of the elderlypeople, a crowd of the people having walking handicap, or the like).Further, the index value calculation unit 2020 may use a model ofcalculating the degree of risk in consideration of external factors suchas the weather. Specifically, the index value calculation unit 2020 usesa model in which the degree of risk becomes high when ambient light isweak due to a bad weather or when the ground surface is wet due to rain.In addition, when it is possible to acquire attributes of a crowd suchas the elderly people, children, or people having walking handicap, thedegree of risk may be calculated also in consideration of information ofthe attributes.

<<Degree of Monitoring>>

The image processing apparatus 2000 may set the degree of that amonitoring target is not monitored (hereinafter, the degree ofinsufficient monitoring) as a state of the monitoring target. Here,suppose that a security guard in the scene performs monitoring in afacility where the camera 3000 is installed. The security guard in thescene may be required to take charge of a wide range by oneself, or maybe required to cope with a visitor during monitoring. For this reason,the degree of that the monitoring target is monitored by the securityguard may vary.

Thus, the index value calculation unit 2020 handles the degree ofinsufficient monitoring of a monitoring target as a state of themonitoring target. For example, the degree of insufficient monitoringcan be calculated on the basis of a distance between the monitoringtarget and a security guard near the monitoring target. Specifically, aconfiguration is provided in which the degree of insufficient monitoringbecomes higher as the distance between the monitoring target and thesecurity guard increases.

As a specific example, the degree of insufficient monitoring can bemodeled by a monotone non-decreasing function f(d), which increases as adistance d from a security guard increases. At this time, the degree ofinsufficient monitoring may also be modeled in consideration of theorientation of the security guard. Specifically, the degree ofinsufficient monitoring is modeled by a function f(d, θ) which isdetermined by the above-mentioned distance d and an absolute value θ ofan angle of a gap between the orientation of the security guard and thedirection to the location of the monitoring target (angle between avector indicating a direction from the position of the security guard tothe monitoring target and a vector indicating the orientation of thesecurity guard). Here, f(d, θ) is set as a monotone non-decreasingfunction for θ. When modeling is performed with the assumption that theinfluence of a distance and the influence of a direction are independentof each other, g(d) and h(θ) are set as a monotone non-decreasingfunction for the distance d and a monotone non-decreasing function forthe absolute value θ of the gap of the angle, respectively, and modelingcan be performed like f(d, θ)=g(d) h(θ).

In addition, the degree of that a security guard is focused on guarding(hereinafter, the degree of focusing on guarding) may be used for thecalculation of the degree of insufficient monitoring. For example, thedegree of focusing on guarding is determined by the state, posture, andthe like of the security guard. For example, when a security guard, whoshould perform guarding with looking around the surrounding, facesdownward or upward, it may be considered that the degree of focusing onguarding of the security guard is low. In addition, when the securityguard performs an operation other than guarding even when the posture ofthe security guard faces the front, it may be considered that the degreeof focusing on guarding of the security guard is low. The operationother than guarding includes, for example, an operation of dealing witha customer, an operation of contacting by a mobile phone, and anoperation of installing a pole.

Here, there are various methods for the index value calculation unit2020 to ascertain the state and posture of a security guard. Forexample, the index value calculation unit 2020 analyzes the state andposture of a security guard in a captured image. In addition, forexample, the index value calculation unit 2020 may ascertain the postureof the security guard by acquiring posture information of a mobile phonefrom the mobile phone the security guard has. For example, the postureinformation of the mobile phone is information regarding accelerationfor each of three-dimensional directions measured by an accelerationsensor included in the mobile phone.

The index value calculation unit 2020 calculates the degree of focusingon guarding which indicates, for example, a value of equal to or greaterthan 0 and equal to or less than 1, in accordance with the state and thelike of the above-mentioned security guard. The index value calculationunit 2020 calculates the degree of insufficient monitoring using a modelsuch as f(d, θ) mentioned above, and calculates the eventual degree ofinsufficient monitoring by multiplying the degree of focusing onguarding of the security guard by the calculated value.

Further, the index value calculation unit 2020 may calculate the degreeof insufficient monitoring in consideration of the above-describeddegree of risk. Specifically, it may be considered that a monitoringtarget is a target to be monitored as the degree of risk is higher.Accordingly, even if the degrees of insufficient monitoring calculatedusing the above-described method are the same as each other, amonitoring target having a higher degree of risk is made to have ahigher degree of insufficient monitoring which is eventually calculated.For example, the index value calculation unit 2020 calculates the degreeof risk and the degree of insufficient monitoring with respect to acertain monitoring target using the above-described method, and sets avalue obtained by multiplying the degrees together as the degree ofinsufficient monitoring which is eventually calculated.

Note that, when there are a plurality of security guards, the indexvalue calculation unit 2020 may calculate the degree of insufficientmonitoring for a certain monitoring target using the degrees ofinsufficient monitoring calculated for each of the security guards. Forexample, the index value calculation unit 2020 calculates the degree ofinsufficient monitoring for a certain monitoring target as statisticalvalue (a minimum value, a maximum value, a mean value, or the like) ofthe degrees of insufficient monitoring for the monitoring targetcalculated for each of the security guards.

Here, the index value calculation unit 2020 may set the above-describeddegree of focusing on guarding as the value of the degree ofinsufficient monitoring.

Second Exemplary Embodiment

FIG. 8 is a block diagram illustrating an image processing apparatus2000 according to a second exemplary embodiment. In FIG. 8, an arrowindicates a flow of information. Further, in FIG. 8, each blockindicates a function-based configuration instead of a hardware-basedconfiguration.

The image processing apparatus 2000 according to the second exemplaryembodiment includes an indication color determination unit 2060. Theindication color determination unit 2060 according to the secondexemplary embodiment determines an indication color for a monitoringtarget on the basis of an index value calculated for the monitoringtarget. A presentation unit 2040 changes the color of a monitoringtarget and the color around the monitoring target in a presentationtarget image to the indication color determined for the monitoringtarget.

For example, the indication color determination unit 2060 changes thedensity of the color of a monitoring target in accordance with thelargeness of the index value of the monitoring target, and therebydetermining an indication color of the monitoring target. For example,the indication color determination unit 2060 increases the density ofthe color of the monitoring target, as the index value is larger. Inanother way, the indication color determination unit 2060 may increasethe density of the color of the monitoring target, as the index value issmaller.

Furthermore, for example, the indication color determination unit 2060expresses a monitoring target by one color and determines the density ofthe color on the basis of the largeness of the index value, and therebydetermining an indication color of the monitoring target. For example,the indication color determination unit 2060 sets the indication colorof the monitoring target as a black having a density based on the indexvalue of the monitoring target. FIG. 9 is a diagram illustrating a colormap in which a black becomes darker on the basis of the largeness of anindex value. In the color map of FIG. 9, represented black is darker asdots become larger (moving to rightwards). In addition, the indicationcolor determination unit 2060 may express an indication color of amonitoring target using any one of RGB colors and may determine thedensity of the color in accordance with the largeness of an index value.For example, the indication color determination unit 2060 sets theindication color of the monitoring target as red, and makes the reddarker as the index value of the monitoring target becomes larger.

Besides, for example, the indication color determination unit 2060 usesa specific color map and determines color corresponding to the indexvalue of the monitoring target with the color map, and sets the color asan indication color of the monitoring target. An example of a color mapused includes a rainbow-colored color map, which is used for a heat mapor the like. A representative rainbow-colored color map is constitutedby gradation of red, orange, yellow, green, blue, indigo, and violet, asillustrated in FIG. 10. In FIG. 10, red, orange, yellow, green, blue,indigo, and violet are set in descending order of an index value.However, the color map used by the indication color determination unit2060 is not limited to the color map illustrated in FIG. 10. Theindication color determination unit 2060 can use any color map. Notethat, the color map used by the indication color determination unit 2060is stored in a storage unit provided inside or outside the imageprocessing apparatus 2000.

Note that, the presentation unit 2040 may change only a portion of thecolor of a monitoring target instead of the entire color of themonitoring target. For example, when a monitoring target is a person,the presentation unit 2040 changes only the color of the face of themonitoring target.

Specific Example

FIGS. 11A and 11B are diagrams conceptually illustrating that a color ofa monitoring target and a color around the monitoring target are changedto a color based on an index value indicating the degree of change inthe position of the monitoring target. Captured images 10-1 and 10-2illustrated in FIGS. 11A and 11B are images obtained by capturing thesame path at different times. The captured image 10-1 illustrated inFIG. 11A is an image captured prior to the captured image 10-2illustrated in FIG. 11B. Comparing the captured images 10-1 and 10-2with each other, the position of a person 20 does not changesignificantly, and the positions of the other people are significantlychanging. Here, it is considered that the staying person is a person whoshould be attentively monitored.

Thus, the indication color determination unit 2060 determines anindication color so that a monitoring target (person) has a darkercolor, as an index value becomes smaller. The presentation unit 2040changes the color of a monitoring target and the color around themonitoring target in the captured image 10-2 to the determinedindication color. As a result, the color of the person 20 and the coloraround the person are dark, and the color of the other people and thecolor around the other people are light. Here, similarly to the case ofFIG. 9, FIGS. 11A and 11B show that color is darker as the size of a dotbecomes larger. In addition, arrows drawn in FIG. 11B are used toillustrate that the person is moving, and it is not necessary to draw anarrow on a real captured image.

<Operational Advantages>

According to the image processing apparatus 2000 of the presentexemplary embodiment, an indication color of a captured image isdetermined on the basis of the degree of change in the state of amonitoring target, and an indication using the indication color ispresented on a presentation target image. For this reason, according tothe image processing apparatus 2000 of the present exemplary embodiment,it is possible to intuitively ascertain the degree of change in thestate of a monitoring target, as compared to a method of indicating anindex value on a presentation target image as it is. Accordingly, anobserver or the like viewing a presentation target image ascertains thecurrent condition of the monitoring target more easily.

Third Exemplary Embodiment

An image processing apparatus 2000 according to a third exemplaryembodiment has the same configuration as that of the image processingapparatus 2000 according to the first or second exemplary embodiment.

A presentation unit 2040 according to the third exemplary embodimentpresents an indication for emphasizing a monitoring target on apresentation target image on the basis of the index value of themonitoring target. For example, the presentation unit 2040 presents anindication for emphasizing a monitoring target more as the index valuethereof becomes larger, or presents an indication for emphasizing amonitoring target more as the index value thereof becomes smaller, on apresentation target image.

<Emphasizing Using Frame>

For example, the presentation unit 2040 presents a frame having athickness depending on the largeness of an index value, around amonitoring target. In this case, for example, the presentation unit 2040calculates a thickness b of a frame using the following Expression (2).Here, “b0” denotes an initial value of the thickness, “I” denotes anindex value calculated by an index value calculation unit 2020, and “α”denotes a proportional constant. Note that, the shape of the frame thatthe presentation unit 2040 presents is arbitrary.

[Expression 2]

b=b ₀ +α·I  (2)

When a monitoring target is emphasized more as the monitoring target hasa larger index value, the presentation unit 2040 makes a frame thickeras the index value thereof becomes larger. In this case, “b0” denotesthe lower limit of the thickness, and “α” denotes a positive realnumber. On the other hand, when a monitoring target is emphasized moreas the index value thereof becomes larger, the presentation unit 2040makes a frame thicker as the index value thereof becomes smaller. Inthis case, “b0” denotes the upper limit of the thickness, and “α”denotes a negative real number.

Note that, the presentation unit 2040 may change the thickness of anoutline of a monitoring target using the same method as the method ofpresenting a frame around a monitoring target. Specifically, thepresentation unit 2040 presents an outline of a monitoring target to beemphasized.

In addition, the presentation unit 2040 may perform emphasizing byblinking a frame at a frequency based on the index value of a monitoringtarget. For example, when the presentation unit 2040 emphasizes amonitoring target more as the index value thereof becomes larger, thepresentation unit further increases the number of blinking per unit time(shortens an interval of blinking) as the index value of a monitoringtarget for which a frame is presented becomes larger. Similarly, whenthe presentation unit 2040 emphasizes a monitoring target more as theindex value thereof becomes smaller, the presentation unit furtherincreases the number of blinking per unit time (shortens an interval ofblinking) as the index value of a monitoring target for which a frame ispresented becomes smaller.

Specific Example

FIGS. 12A and 12B are diagrams conceptually illustrating thatemphasizing is performed by presenting a frame around a monitoringtarget. Captured images 10-1 and 10-2 illustrated in FIGS. 12A and 12Bare images obtained by capturing a queue of people in the same place atdifferent times. Similarly to the cases of FIGS. 11A and 11B, thecaptured image 10-1 is an image captured prior to the captured image10-2. Comparing the captured images 10-1 and 10-2 with each other, thelength of an upper queue 30-1 does not change, and the length of a lowerqueue 30-2 significantly changes. Here, it is preferable that the lengthof the queue reduces along with time, and it is considered that a queuehaving a small degree of change in length should be observed carefully.

Thus, the presentation unit 2040 presents a frame around a monitoringtarget (person) so that the thickness of frame becomes larger as theindex value thereof becomes smaller. In FIGS. 12A and 12B, a thickerframe is presented around of the queue 30-1, and a thinner frame ispresented around the queue 30-2.

<Emphasizing Using Color>

In addition, an image processing apparatus 2000 according to the thirdexemplary embodiment may present an indication for emphasizing amonitoring target by changing the color of the monitoring target or thecolor around the monitoring target into an indication color, which isdetermined for the monitoring target, using the indication colordetermination unit 2060 described in the second exemplary embodiment.For example, the index value calculation unit 2020 emphasizes themonitoring target by increasing the density of the indication color ofthe monitoring target. In addition, the indication color determinationunit 2060 constitutes an indication color of a monitoring target using acolor map constituted by colors, which color is more noticeable as thecolor more closely corresponds to the index value of the monitoringtarget to be emphasized. For example, when a monitoring target isemphasized more as the index value thereof becomes larger, theindication color determination unit 2060 uses a color map having colors,which color is more noticeable (red or the like) as the colorcorresponds to a larger index value and which color is less noticeable(gray or the like) as the color corresponds to a smaller index value.

Here, it is also possible to realize the changing of the color around amonitoring target into a certain color by presenting a frame having thecolor near the monitoring target. In this case, the presentation unit2040 may make the thickness of the frame constant regardless of an indexvalue or may make the thickness of the frame vary depending on an indexvalue. A method of determining the thickness of a frame depending on anindex value is as described above.

Specific Example

FIGS. 13A and 13B are diagrams conceptually illustrating thatemphasizing is performed by presenting a frame having a color and sizebased on an index value around a monitoring target. Captured images 10-1and 10-2 illustrated in FIGS. 13A and 13B are images obtained bycapturing a crowd in the same place at different times. Similarly to thecases of FIGS. 11A and 11B and FIGS. 12A and 12B, the captured image10-1 is an image captured prior to the captured image 10-2. Comparingthe captured images 10-1 and 10-2 with each other, the number of peopleincluded in upper right crowd 40-1 increases, and the number of peopleincluded in a lower left crowd 40-2 decreases.

In this case, the indication color determination unit 2060 determines anindication color so that the color of a crowd becomes darker as thenumber of people in the crowd increases. In addition, the presentationunit 2040 determines the thickness of a frame so that the frame becomesthicker as the degree of increase in the number of people of the crowdbecomes higher. As a result, the presentation unit 2040 presents a thickand dark frame around the crowd 40-1 in which the number of peoplesignificantly increases, and presents a thin and light frame around thecrowd 40-2 in which the number of people does not significantlyincrease.

<Operational Advantages>

According to the image processing apparatus 2000 of the presentexemplary embodiment, an indication, which is for emphasizing amonitoring target to the extent based on the index value of a monitoringtarget, is presented on a presentation target image. Therefore, anobserver or the like viewing the presentation target image canimmediately ascertain the degree of change in each monitoring target andcan immediately ascertain to what extent each monitoring target shouldbe monitored attentively.

Fourth Exemplary Embodiment

FIG. 14 is a block diagram illustrating an image processing apparatus2000 according to a fourth exemplary embodiment. In FIG. 14, an arrowindicates a flow of information. Further, in FIG. 14, each blockindicates a function-based configuration instead of a hardware-basedconfiguration.

The image processing apparatus 2000 according to the fourth exemplaryembodiment presents an indication on a first image on the basis of howmuch the degree of change in the state of a monitoring target deviatesfrom a reference degree of change. Thereby, the image processingapparatus 2000 according to the fourth exemplary embodiment includes adivergence degree calculation unit 2080.

The divergence degree calculation unit 2080 calculates the degree ofdivergence between an index value calculated by an index valuecalculation unit 2020 and a reference degree of change. A presentationunit 2040 according to the fourth exemplary embodiment presents anindication for emphasizing a monitoring target more as the degree ofdivergence thereof becomes higher, on a monitoring target.

Here, the divergence degree calculation unit 2080 acquires a referencedegree of change from a storage unit provided inside or outside theimage processing apparatus 2000. Here, the reference degree of changemay vary according to what is handled as the state of a monitoringtarget. In this case, the storage unit may store the reference degree ofchange for each state of a monitoring target.

<Method of Calculating Degree of Divergence>

There are various methods for the divergence degree calculation unit2080 to calculate the degree of divergence. For example, the divergencedegree calculation unit 2080 calculates a degree of divergence k usingthe following Expression (3). Here, “I” denotes an index valuecalculated for a monitoring target, and “I_(base)” denotes a referencedegree of change. However, a method of calculating a degree ofdivergence is not limited to the following method.

$\begin{matrix}\left\lbrack {{Expression}\mspace{14mu} 3} \right\rbrack & \; \\{k = \frac{I - I_{base}}{I_{base}}} & (3)\end{matrix}$

<Emphasizing Using Color>

For example, the image processing apparatus 2000 according to the fourthexemplary embodiment changes the color of a monitoring target on thebasis of the degree of divergence. In this case, the image processingapparatus 2000 according to the fourth exemplary embodiment includes anindication color determination unit 2060.

The indication color determination unit 2060 according to the fourthexemplary embodiment determines the color of a monitoring target and thecolor around the monitoring target using the same method as that used bythe indication color determination unit 2060 described in the secondexemplary embodiment. For example, the indication color determinationunit 2060 determines the density of the color of a monitoring target onthe basis of the degree of divergence calculated for the monitoringtarget. In this case, the indication color determination unit 2060minimizes the density when the degree of divergence is 0, and makes thecolor of the monitoring target darker as the degree of divergencebecomes higher. Note that, when this method is used, the degree ofdivergence is expressed by an absolute value of divergence between anindex value and a reference value if a negative value can be taken forthe index value. For example, the degree of divergence is expressed byan absolute value of a value calculated using Expression (3).

In addition, the indication color determination unit 2060 sets thedensity of the color of a monitoring target when the degree ofdivergence is 0 as a reference density. The indication colordetermination unit makes the color of a monitoring target darker as thedegree of divergence becomes higher in a positive direction (becomeslarger than a reference value), and makes the color of the monitoringtarget lighter as the degree of divergence becomes higher in a negativedirection (becomes smaller than the reference value). FIG. 15 is adiagram conceptually illustrating a method of determining the density ofan indication color on the basis of the degree of divergence when areference density is determined. For example, the indication colordetermination unit 2060 sets the density of the color corresponding to areference degree of change as the density of the original color of amonitoring target. In other words, when the degree of divergence is 0,the density of the color of the monitoring target does not change. Theindication color determination unit 2060 makes the color of a monitoringtarget darker than the original color when an index value is larger thana reference degree of change (when the degree of divergence has apositive value), and makes the color of the monitoring target lighterthan the original color when the index value is smaller than thereference degree of change (when the degree of divergence has a negativevalue).

Note that, when using the density of any one of RGB colors described inthe second exemplary embodiment or using a specific color map, a methodof determining an indication color on the basis of the degree ofdivergence is also the same as the method of changing the density of thecolor of a monitoring target on the basis of the above-mentioned degreeof divergence.

<Indication for Emphasizing>

The presentation unit 2040 may present an indication for emphasizing amonitoring target on the basis of the degree of divergence calculatedfor the monitoring target, using the same method as the method describedin the third exemplary embodiment.

<Emphasizing Using Frame>

For example, as is the case with the third exemplary embodiment, thepresentation unit 2040 performs emphasizing using a frame and color. Inthis case, for example, the presentation unit 2040 determines athickness b′ of a frame of a monitoring target according to Expression(4). Here, “k” denotes the above-mentioned degree of divergence. Forexample, when a is set to be a positive real number, the frame becomesthicker as the degree of divergence becomes higher.

[Expression 4]

b′=b ₀ +α·k  (4)

In addition, as is the case with the third exemplary embodiment, thepresentation unit 2040 may perform emphasizing by changing the thicknessof an outline of a monitoring target in accordance with the degree ofdivergence or by blinking the frame at a frequency based on the degreeof divergence.

<Emphasizing Using Color>

Similarly to the indication color determination unit 2060 according tothe third exemplary embodiment, the indication color determination unit2060 according to the fourth exemplary embodiment may present anindication for emphasizing a monitoring target by changing an indicationcolor of the monitoring target. Specifically, when the indication colordetermination unit 2060 emphasizes a monitoring target more as thedegree of divergence thereof becomes higher, the indication colordetermination unit determines an indication color by making theindication color darker as the degree of divergence becomes higher or byusing a color map constituted by colors, which color is more noticeableas the degree of divergence of the monitoring target becomes higher.Similarly, when the indication color determination unit 2060 emphasizesa monitoring target more as the degree of divergence thereof becomeslower, the indication color determination unit determines an indicationcolor by making the indication color darker as the degree of divergencebecomes lower or by using a color map constituted by colors, which coloris more noticeable as the color corresponds to a lower degree ofdivergence.

<Operational Advantages>

According to the present exemplary embodiment, an indication foremphasizing a monitoring target is presented on a presentation targetimage on the basis of how much the degree of change in the state of amonitoring target deviates from a reference degree of change. It ispossible to more accurately obtain the degree to which the monitoringtarget should be emphasized by introducing the reference degree ofchange. Accordingly, an observer or the like can more accuratelyascertain the degree to which monitoring should be attentivelyperformed, with respect to each monitoring target.

Fifth Exemplary Embodiment

The configuration of an image processing apparatus 2000 according to afifth exemplary embodiment is shown by FIG. 1, as is the case with thefirst exemplary embodiment.

An index value calculation unit 2020 according to the fifth exemplaryembodiment calculates a predicted value of the degree of change in thestate of a monitoring target, on the basis of the calculated degree ofchange in the state of the monitoring target, at and after the time wheneach image used for the calculation is captured. The index valuecalculation unit 2020 sets the predicted value calculated for themonitoring target as the index value of the monitoring target.

For example, the index value calculation unit 2020 calculates apredicted value of the degree of change in the state of a monitoringtarget after predetermined time of time t, by using a plurality ofimages captured over a predetermined period of time in the past from thetime t. An indication based on the predicted value is presented on apresentation target image, which is presented on a display screen at thetime t.

For example, the index value calculation unit 2020 generates a model forpredicting the state of a monitoring target using the acquired pluralityof captured images. Note that, since a method of generating a predictingmodel from a sample value is a known method, the detailed descriptionthereof will not be described here. The index value calculation unit2020 calculates a predicted value of the degree of change in the stateof the monitoring target at and after the time of capturing the imagesused for the generation of the model, using the model for predicting thestate of the monitoring target generated from the acquired plurality ofcaptured images.

Suppose that a model for predicting the state of a monitoring target isexpressed by f(t). Here, “t” denotes time, and “f(t)” denotes apredicted value of the state of the monitoring target at the time t. Inthis case, for example, the index value calculation unit 2020 calculatesthe degree of change in the state of the monitoring target between timet1 and time t2 in the future, using the following Expression (5). Here,“a” denotes a predicted value of the degree of change in the state ofthe monitoring target. Note that, the following Expression (5) is justan example, and the method of calculating a predicted value is notlimited to a method using Expression (5).

$\begin{matrix}\left\lbrack {{Expression}\mspace{14mu} 5} \right\rbrack & \; \\{a = \frac{{f\left( t_{2} \right)} - {f\left( t_{1} \right)}}{t_{2} - t_{1}}} & (5)\end{matrix}$

Note that, “t1” may be time in the future, may be the current time, ormay be time in the past. When t1 is the current time or time in thepast, the value of f(t) may be calculated on the basis of a measuredvalue instead of being calculated using a predicting model.

In addition, the index value calculation unit 2020 may calculate apredicted value of the degree of change in the state of a monitoringtarget using a predicting model provided in advance. In this case, theindex value calculation unit 2020 uses the state of each monitoringtarget in an acquired captured image as an input to the predictingmodel. The predicting model may be stored inside or outside the indexvalue calculation unit 2020.

Further, when an indication based on a predicted value is presented onan image captured by a certain camera 3000-1, the index valuecalculation unit 2020 may use an image captured by another camera 3000located around the camera 3000-1. For example, the index valuecalculation unit 2020 analyzes an image captured by a camera 3000-2adjacent to the camera 3000-1. As a result, suppose that a crowd isheading toward a place within an imaging range of the camera 3000-1. Inthis case, the presentation unit 2040 presents an indication based on apredicted value of the degree of change in the state calculated for thecrowd, in a region to which the crowd is predicted to move in the imagecaptured by the camera 3000-1. Specifically, the presentation unit 2040performs a process of changing the color of the region into which thecrowd is predicted to flow in the image captured by the camera 3000-1 ora process of surrounding the region with a frame.

<Operational Advantages>

According to the present exemplary embodiment, an indication based on apredicted value of the degree of change in the state of a monitoringtarget is presented on a presentation target image. Therefore, anobserver or the like can immediately ascertain that the future action ofa monitoring target should be observed carefully.

Sixth Exemplary Embodiment

In a sixth exemplary embodiment, in terms of images captured by acertain camera 3000 (hereinafter, a camera 3000-1), there are a periodof time during which they are displayed on a display screen 4000, andanother period of time during which they are not displayed thereon. Forexample, there is a case where images captured by the plurality ofcameras 3000 are displayed on one display screen 4000 in a time-divisionmanner.

Here, when the images captured by the camera 3000-1 are not displayedduring a period of time and are displayed after the period of time onthe display screen 4000, an index value calculation unit 2020 accordingto the sixth exemplary embodiment calculates an index value of amonitoring target on the basis of the degree of change in the state ofthe monitoring target before and after that period of time.

FIG. 16 is a diagram conceptually illustrating that images captured bythe plurality of cameras 3000 are displayed on the display screen 4000in a time-division manner. In the case of FIG. 16, images captured bythe camera 3000-1 are displayed during periods of time p1 and p3, andimages captured by another camera 3000-2 are displayed during a periodof time p2. In this case, the index value calculation unit 2020calculates an index value on the basis of the degree of change betweenthe state of a monitoring target before the period of time p2 and thestate of the monitoring target after the period of time p2. Hereinafter,a period of time (p2 or the like in FIG. 16) during which the imagescaptured by the camera 3000-1 are not displayed on the display screen4000 is described as a non-displayed period.

For example, the index value calculation unit 2020 calculates an indexvalue used for presentation after the elapse of a non-displayed period,using a predetermined number of captured images presented on the displayscreen 4000 before the non-displayed period and captured images for apredetermined time (a predetermined number of captured images) which arepresented on the display screen 4000 after the non-displayed period.FIG. 17 is a diagram illustrating a method for the index valuecalculation unit 2020 to calculate an index value, according to thesixth exemplary embodiment. Periods of time p1, p2, and p3 are the sameas those in FIG. 16. The index value calculation unit 2020 calculates anindex value of a monitoring target using captured images displayed onthe display screen 4000 during a period of time p4, which is a portionof the period of time p1, and captured images displayed on the displayscreen 4000 during a period of time p5, which is a portion of the periodof time p3. The presentation unit 2040 presents an indication based onthe calculated index value on a presentation target image displayed onthe display screen 4000 at time t. Note that, the length of the periodof time p4 and the length of the period of time p5 may be the same aseach other or may be different from each other.

For example, the presentation unit 2040 continues presenting anindication based on the degree of change in the state of a monitoringtarget between the period of time p4 and the period of time p5 oncaptured images for a predetermined period of time (for example, for tenseconds) from the time t in order for an observer or the like to be ableto sufficiently ascertain the degree of change in the state of themonitoring target before and after the period of time p2.

FIG. 18 is a flow chart illustrating a flow of processing performed bythe image processing apparatus 2000 according to the sixth exemplaryembodiment. In step S202, the display screen 4000 displays imagescaptured by the camera 3000-1. In step S204, an indication target of thedisplay screen 4000 is switched from the camera 3000-1 to the camera3000-2. In step S206, the display screen 4000 displays images capturedby the camera 3000-2. In step S208, the display target of the displayscreen 4000 is switched from the camera 3000-2 to the camera 3000-1.

In step S210, the index value calculation unit 2020 calculates an indexvalue indicating the degree of change between the state of a monitoringtarget that was displayed in S202 and the state of a monitoring targetthat will be displayed from now on. In step S212, the index valuecalculation unit 2020 presents an indication based on the calculatedindex value on the images captured by the camera 3000-1. The capturedimages on which the indication is presented are displayed on the displayscreen 4000.

Note that, if a period of time during which the images captured by thecamera 3000-1 are not displayed on the display screen 4000 is shorterthan a predetermined period of time, the index value calculation unit2020 may regard that as “the images captured by the camera 3000-1 arecontinuously being displayed on the display screen 4000”. This isbecause it is considered that, for example, an observer may be regardedas having been continuously viewing an image of the same camera, if acamera of the display target is switched to another camera for merely ashort period of time, such as approximately one second.

<Operational Advantages>

According to the present exemplary embodiment, when images captured bythe camera 3000-1 are not displayed during a period of time and aredisplayed after the period of time on the display screen 4000, an indexvalue indicating the degree of change in the state of a monitoringtarget before and after the monitoring target is calculated. In thismanner, for example, when the channel of the display screen 4000 isswitched again to the images of the camera 3000-1 after the channel ofthe display screen 4000 is switched from the images of the camera 3000-1to the images of the camera 3000-2, an observer or the like canimmediately ascertain how much the state of each monitoring target haschanged, compared to when the image of the camera 3000-1 was viewed lasttime. Accordingly, even when it is difficult to continue monitoring onlyimages captured by a specific camera 3000, it is possible to immediatelyascertain the degree of change in the state of a monitoring targetcaptured by a certain camera 3000 at the time of viewing images capturedby the camera 3000.

Seventh Exemplary Embodiment

An image processing apparatus 2000 according to a seventh exemplaryembodiment is shown by FIG. 1, similar to the image processing apparatus2000 according to the first exemplary embodiment.

For example, there is a case where an observer or the like viewing adisplay screen 4000 cannot carefully observe the entire display screen4000 at one time, such as a case where the display screen 4000 has alarge size. Thus, an index value calculation unit 2020 according to theseventh exemplary embodiment calculates an index value indicating thedegree of change in the state of a monitoring target before and after aperiod of time during which a certain partial region of the displayscreen 4000 (hereinafter, a first partial region) has not correspondedto the direction of the eye gaze of a user (observer or the like), withrespect to the monitoring target displayed in the first partial region.A presentation unit 2040 according to the seventh exemplary embodimentpresents an indication based on the calculated index value on a regiondisplayed in the first partial region in the captured image displayedafter the above-mentioned period of time.

FIG. 19 is a diagram illustrating a relationship between a user's eyegaze direction and a partial region. In FIG. 19, an eye gaze direction50-1 corresponds to a partial region 60-1, and an eye gaze direction50-2 corresponds to a partial region 60-2. Note that, for the purpose ofsimplifying the drawing, an eye gaze direction corresponding to apartial region is shown by one arrow, but an eye gaze directioncorresponding to a partial region actually has a certain degree ofwidth. For example, the eye gaze direction 50-1 may be an eye gazedirection with which the partial region 60-1 is included in a user'seyesight so that the user can carefully observe a monitoring targetincluded in the partial region 60-1.

A basic principle of a process performed by the index value calculationunit 2020 according to the seventh exemplary embodiment is the same asthe principle of the process performed by the index value calculationunit 2020 according to the sixth exemplary embodiment. Specifically, theindex value calculation unit 2020 handles “a period of time during whicha partial region is included in a region corresponding to the user's eyegaze direction” in the same manner as “a period of time during whichimages captured by a camera 3000-1 are displayed on the display screen4000” in the sixth exemplary embodiment. In addition, the index valuecalculation unit 2020 handles “a period of time during which the partialregion is not included in the region corresponding to the user's eyegaze direction” in the same manner as “a period of time during which theimages captured by the camera 3000-1 are not displayed on the displayscreen 4000” in the sixth exemplary embodiment.

<Acquisition of User's Eye Gaze Direction>

The index value calculation unit 2020 acquires a user's eye gazedirection. For example, the eye gaze direction is represented by acombination of “an angle in a horizontal direction and an angle in avertical direction”. Here, a reference of each of the angle in thehorizontal direction and the angle in the vertical direction (directionfor setting 0 degrees) is arbitrary.

For example, the user's eye gaze direction is calculated by capturingthe face and eyes of the user using a camera or the like and analyzingthe captured image. The camera capturing the face and eyes of the useris installed, for example, near the display screen 4000. Since atechnique of capturing images of the face and eyes of a user and therebydetecting an eye gaze direction are known techniques, the detaileddescription thereof will not be described here. Note that, a processingunit detecting the user's eye gaze direction (hereinafter, an eye gazedirection detection unit) may be provided inside or outside the imageprocessing apparatus 2000.

<Specific Method>

For example, the index value calculation unit 2020 handles the displayscreen 4000 by dividing the display screen into a predetermined numberof partial regions. The index value calculation unit 2020 acquires theobserver's eye gaze direction from the eye gaze direction detection unitand determines to which partial region the eye gaze directioncorresponds. When the determined partial region is different from apartial region determined last time, it is ascertained that the partialregion corresponding to the user's eye gaze direction has changed.

FIG. 20 is a diagram illustrating information in which a partial regioncorresponding to an observer's eye gaze direction and the time when theobserver's eye gaze direction has changed, in a table format. The tableis named as an eye-gaze information table 100. The eye-gaze informationtable 100 includes two columns of a time 102 and a partial region ID104. In each record of the eye-gaze information table 100, an ID of apartial region included in the user's eye gaze direction from the timeshown in the point in time 102 is shown in the partial region ID 104.

In FIG. 20, at the time t1 and the time t4, a region corresponding tothe observer's eye gaze direction is a partial region 1. Thus, the indexvalue calculation unit 2020 calculates an index value indicating thedegree of change between a state of a monitoring target during a periodof time between the time t1 and the time t2 (a period of time duringwhich the partial region 1 corresponds to the user's eye gaze direction)and a state of the monitoring target at and after the time t4 (at andafter the time when the partial region 1 corresponds to the user's eyegaze direction again), as an index value of the monitoring target.

Note that, if a period of time during which the user's eye gazedirection is changed to another partial region side is shorter than apredetermined period of time, the index value calculation unit 2020 mayregard that as “the user having continuously viewed the same partialregion”. This is because it is considered that, for example, “anobserver may be regarded as having been continuously viewing a certainpartial region” if the observer takes her/his eyes off the partialregion for a short period of time, such as approximately one second.

In addition, the index value calculation unit 2020 may use theorientation of a user's face instead of the user's eye gaze direction. Amethod of acquiring and using the orientation of the user's face is thesame as a method of detecting and using the user's eye gaze direction.

<Operational Advantages>

According to the present exemplary embodiment, when there is a period oftime during which a certain region is not monitored, it presents anindication indicating the degree of change in the state of eachmonitoring target from the time when the region was viewed last time onthe display screen 4000 when the region is viewed again. Therefore, anobserver or the like can immediately ascertain the degree of change inthe state of a monitoring target in each region even when the entireregion of the display screen 4000 cannot be monitored at one time.

Modification Example 7-1

An image processing apparatus 2000 according to a modification example7-1 described below may be realized so as to have the same configurationas the image processing apparatus 2000 according to the seventhexemplary embodiment. In the image processing apparatus 2000 accordingto the modification example 7-1, a display screen 4000 includes aplurality of small screens 4100. Images captured by different cameras3000 are displayed on the respective small screens 4100.

An index value calculation unit 2020 according to the modificationexample 7-1 calculates an index value indicating the degree of change inthe state of a monitoring target before and after a period of timeduring which a certain small screen 4100-1 is not included in a regioncorresponding to the user's eye gaze direction, with respect to themonitoring target displayed on the small screen 4100-1. A presentationunit 2040 according to the modification example 7-1 presents anindication based on the calculated index value on a captured imagedisplayed on the small screen 4100-1 after that period of time.

The small screen 4100 can be handled in the same manner as the partialregion in the seventh exemplary embodiment. For this reason, a basicprinciple of a process performed by the index value calculation unit2020 according to the modification example 7-1 is the same as theprinciple of the process performed by the index value calculation unit2020 according to the seventh exemplary embodiment. Specifically, theindex value calculation unit 2020 handles “a period of time during whichthe small screen 4100-1 is included in the user's eye gaze direction” inthe same manner as “the period of time during which the partial regionis included in the user's eye gaze direction” in the seventh exemplaryembodiment. In addition, the index value calculation unit 2020 handles“a period of time during which the small screen 4100-1 is not viewed byan observer” in the same manner as “the period of time during which thepartial region is not included in the user's eye gaze direction” in theseventh exemplary embodiment.

The exemplary embodiments of the invention have been described so farwith reference to the accompanying drawings. However, the exemplaryembodiments are merely illustrative of the invention, and other variousconfigurations can also be adopted.

Hereinafter, examples of reference configurations will be added.

-   -   (1) An image processing apparatus including:

an index value calculation unit calculating an index value indicating adegree of change in a state of a monitoring target in a plurality ofcaptured images using the captured images, the captured images beingcaptured by a camera at different times; and

a presentation unit presenting an indication based on the index value ona first captured image captured by the camera.

(2) The image processing apparatus according to (1), further including afirst indication color determination unit determining an indicationcolor based on the index value, with respect to the monitoring target,

wherein the presentation unit changes a color of the monitoring targetor a color around the monitoring target into the indication colordetermined for the monitoring target, in the first captured image.

(3) The image processing apparatus according to (1) or (2), wherein thepresentation unit presents an indication for emphasizing a monitoringtarget more as the index value of the monitoring target becomes larger,or presents an indication for emphasizing a monitoring target more asthe index value of the monitoring target becomes smaller.

(4) The image processing apparatus according to (1), further including adivergence degree calculation unit calculating a degree of divergencebetween the index value and a reference degree of change,

wherein the presentation unit presents an indication for emphasizing amonitoring target more as the degree of divergence of the monitoringtarget becomes higher, or presents an indication for emphasizing amonitoring target more as the degree of divergence of the monitoringtarget becomes lower in the first captured image.

(5) The image processing apparatus according to (4), further including asecond indication color determination unit determining an indicationcolor based on the degree of divergence calculated for the monitoringtarget, with respect to the monitoring target,

wherein the presentation unit changes a color of the monitoring targetor a color around the monitoring target into the indication colordetermined for the monitoring target, in the first captured image.

(6) The image processing apparatus according to any one of (1) to (5),

wherein the index value calculation unit calculates a predicted value ofthe degree of change in the state of the monitoring target at and aftera time when each image used for the calculation is captured, thecalculation of the predicted value being performed using the calculateddegree of change in the state of the monitoring target, and

wherein the index value calculation unit sets the predicted value as theindex value.

(7) The image processing apparatus according to any one of (1) to (6),

wherein when an image captured by the camera is not displayed during acertain period of time on a display screen for displaying the capturedimage, the index value calculation unit calculates the index valueindicating a degree of change between a state of a monitoring targetpresented before the period of time and a state of the monitoring targetpresented after the period of time, and

wherein the presentation unit uses a captured image displayed after theperiod of time as the first captured image.

(8) The image processing apparatus according to any one of (1) to (7),

wherein when a first partial region of a display screen for displayingthe captured image is not included in a screen region corresponding toan eye gaze direction or a face direction of a user viewing the displayscreen during a certain period of time, the index value calculation unitcalculates an index value indicating a degree of change between a stateof the monitoring target presented on a first partial region before theperiod of time and a state of the monitoring target presented in thefirst partial region after the period of time, and

wherein the presentation unit presents an indication based on the indexvalue calculated for the first partial region on a region presented inthe first partial region in the first captured image, using the capturedimage presented after the period of time as the first captured image.

(9) The image processing apparatus according to any one of (1) to (8),wherein the index value calculation unit calculates an index valueindicating a degree of change in a position of the monitoring target.

(10) The image processing apparatus according to any one of (1) to (9),wherein the index value calculation unit calculates an index valueindicating a degree of change in a frequency at which the monitoringtarget is shown in the image.

(11) The image processing apparatus according to any one of (1) to (10),wherein the index value calculation unit calculates an index valueindicating a degree of change in a degree of crowdedness of a pluralityof objects included in the monitoring target.

(12) The image processing apparatus according to any one of (1) to (11),

wherein the monitoring target includes a queue of objects, and

wherein the index value calculation unit calculates an index valueindicating a degree of change in length or speed of the queue.

(13) The image processing apparatus according to any one of (1) to (12),wherein the index value calculation unit calculates an index valueindicating a degree of change in the number of objects included in themonitoring target.

(14) The image processing apparatus according to any one of (1) to (13),

wherein the monitoring target includes a person, and

wherein the index value calculation unit calculates an index valueindicating a degree of change in a degree of dissatisfaction of themonitoring target, as the index value of the monitoring target.

(15) The image processing apparatus according to any one of (1) to (14),

wherein the monitoring target includes a person or a place, and

wherein the index value calculation unit calculates an index valueindicating a degree of change in a degree of risk of the monitoringtarget, as the index value of the monitoring target.

(16) The image processing apparatus according to any one of (1) to (15),

wherein the monitoring target includes a person or a place, and

wherein the index value calculation unit calculates an index valueindicating a degree of change of how sufficiently the monitoring targetis monitored, as the index value of the monitoring target.

(17) A monitoring system including:

a camera;

a display screen; and

the image processing apparatus according to any one of (1) to (16),

wherein the camera generates a plurality of captured images byperforming image capturing at different times, and

wherein the display screen displays the first captured image on which anindication based on the index value is presented by the presentationunit.

(18) An image processing method performed by a computer, the methodincluding:

calculating an index value indicating a degree of change in a state of amonitoring target in a plurality of captured images using the capturedimages, the captured images being captured by a camera at differenttimes; and

presenting an indication based on the index value on a first capturedimage captured by the camera.

(19) The image processing method according to (18), further includingdetermining an indication color based on the index value, with respectto the monitoring target, wherein the step of presenting an indication

includes changing a color of the monitoring target or a color around themonitoring target into the indication color determined for themonitoring target, in the first captured image.

(20) The image processing method according to (18) or (19), wherein thestep of presenting an indication includes presenting an indication foremphasizing a monitoring target more as the index value thereof becomeslarger, or presents an indication for emphasizing a monitoring targetmore as the index value thereof becomes smaller.

(21) The image processing method according to (18), further includingcalculating a degree of divergence between the index value and areference degree of change,

wherein the step of presenting an indication includes presenting anindication for emphasizing a monitoring target more as the degree ofdivergence thereof becomes higher, or presenting an indication foremphasizing a monitoring target more as the degree of divergence thereofbecomes lower in the first captured image.

(22) The image processing method according to (21), further includingdetermining an indication color based on the degree of divergencecalculated for the monitoring target, with respect to the monitoringtarget,

wherein the step of presenting an indication includes changing a colorof the monitoring target or a color around the monitoring target intothe indication color determined for the monitoring target, in the firstcaptured image.

(23) The image processing method according to any one of (18) to (22),

wherein the step of calculating an index value includes calculating apredicted value of the degree of change in the state of the monitoringtarget at and after a time when each image used for the calculation iscaptured, the calculation of the predicted value being performed usingthe calculated degree of change in the state of the monitoring target,and

wherein the step of calculating an index value includes setting thepredicted value as the index value.

(24) The image processing method according to any one of (18) to (23),

wherein when an image captured by the camera is not displayed during acertain period of time on a display screen for displaying the capturedimage, the step of calculating an index value includes calculating theindex value indicating a degree of change between a state of amonitoring target presented before the period of time and a state of themonitoring target presented after the period of time, and

wherein the step of presenting an indication includes using a capturedimage displayed after the period of time as the first captured image.

(25) The image processing method according to any one of (18) to (24),

wherein when a first partial region of a display screen for displayingthe captured image is not included in a screen region corresponding toan eye gaze direction or a face direction of a user viewing the displayscreen during a certain period of time, the step of calculating an indexvalue includes calculating an index value indicating a degree of changebetween a state of the monitoring target presented on a first partialregion before the period of time and a state of the monitoring targetpresented in the first partial region after the period of time, and

wherein the step of presenting an indication includes presenting anindication based on the index value calculated for the first partialregion on a region presented in the first partial region in the firstcaptured image, using the captured image presented after the period oftime as the first captured image.

(26) The image processing method according to any one of (18) to (25),wherein the step of calculating an index value includes calculating anindex value indicating a degree of change in a position of themonitoring target.

(27) The image processing method according to any one of (18) to (26),wherein the step of calculating an index value includes calculating anindex value indicating a degree of change in a frequency at which themonitoring target is shown in the image.

(28) The image processing method according to any one of (18) to (27),wherein the step of calculating an index value includes calculating anindex value indicating a degree of change in a degree of crowdedness ofa plurality of objects included in the monitoring target.

(29) The image processing method according to any one of (18) to (28),

wherein the monitoring target includes a queue of objects,

wherein the step of calculating an index value includes calculating anindex value indicating a degree of change in length or speed of thequeue.

(30) The image processing method according to any one of (18) to (29),wherein the step of calculating an index value includes calculating anindex value indicating a degree of change in the number of objectsincluded in the monitoring target.

(31) The image processing method according to any one of (18) to (30),

wherein the monitoring target includes a person, and

wherein the step of calculating an index value includes calculating anindex value indicating a degree of change in a degree of dissatisfactionof the monitoring target, as the index value of the monitoring target.

(32) The image processing method according to any one of (18) to (31),

wherein the monitoring target includes a person or a place, and

wherein the step of calculating an index value includes calculating anindex value indicating a degree of change in a degree of risk of themonitoring target, as the index value of the monitoring target.

(33) The image processing method according to any one of (18) to (32),

wherein the monitoring target includes a person or a place, and

wherein the step of calculating an index value includes calculating anindex value indicating a degree of change of how sufficiently themonitoring target is monitored, as the index value of the monitoringtarget.

(34) A program causing a computer to operate as the image processingapparatus according to any one of (1) to (16).

(35) An image processing apparatus comprising:

a calculation unit calculating a degree of change in a state of amonitoring target in a plurality of captured images using the capturedimage, the captured images being captured by a camera at differenttimes; and

a presentation unit changing a color of a region of the captured imageinto a color based on the calculated degree of change, the regionshowing the monitoring target.

(36) An image processing apparatus comprising:

a calculation unit calculating a degree of change in a state of amonitoring target in a plurality of captured images using the capturedimage, the captured images being captured by a camera at differenttimes; and

a presentation unit emphasizing a region of the captured image, theregion showing the monitoring target.

This application claims priority from Japanese Patent Application No.2014-134786, filed on Jun. 30, 2014, the entire contents of which areincorporated herein.

1-21. (canceled)
 22. A monitoring system comprising at least oneprocessor configured to execute instructions to perform: calculating anindex value indicating a degree of change in a state of a monitoringtarget in a plurality of captured images, the captured images beingcaptured at different times; and presenting an indication based on theindex value on a first captured image, wherein a train is captured inthe captured images, the degree of change is calculated based on thestate of the monitoring target at times when a door of the train opensand when the door of the train closes.
 23. The monitoring systemaccording to claim 22, wherein the at least one processor is furtherconfigured to perform calculating the index value so as to indicate thedegree of change in a degree of crowdedness of a plurality of objectsincluded in the monitoring target.
 24. The monitoring system accordingto claim 22, wherein the monitoring target includes a queue or a crowdof objects, the at least one processor is further configured to performcalculating the index value so as to indicate the degree of change inlength or speed of the queue or the crowd.
 25. The monitoring systemaccording to claim 22, wherein the at least one processor is furtherconfigured to perform: determining an indication color based on theindex value, with respect to the monitoring target; and changing a colorof the monitoring target or a color around the monitoring target intothe indication color determined for the monitoring target, in the firstcaptured image.
 26. The monitoring system according to claim 22, whereinthe at least one processor is further configured to perform presentingan indication for emphasizing a monitoring target more as the indexvalue of the monitoring target becomes larger, or presenting anindication for emphasizing a monitoring target more as the index valueof the monitoring target becomes smaller.
 27. The monitoring systemaccording to claim 22, wherein the at least one processor is furtherconfigured to perform: calculating a degree of divergence between theindex value and a reference degree of change; and presenting anindication for emphasizing a monitoring target more as the degree ofdivergence of the monitoring target becomes higher, or presenting anindication for emphasizing a monitoring target more as the degree ofdivergence of the monitoring target becomes lower in the first capturedimage.
 28. The monitoring system according to claim 27, wherein the atleast one processor is further configured to perform: determining anindication color based on the degree of divergence calculated for themonitoring target, with respect to the monitoring target; and changing acolor of the monitoring target or a color around the monitoring targetinto the indication color determined for the monitoring target, in thefirst captured image.
 29. The monitoring system according to claim 22,wherein the at least one processor is further configured to perform:calculating a predicted value of the degree of change in the state ofthe monitoring target at and after a time when each image used for thecalculation is captured, the calculation of the predicted value beingperformed using the calculated degree of change in the state of themonitoring target; and setting the predicted value as the index value.30. The monitoring system according to claim 22, wherein the at leastone processor is further configured to perform: when an image capturedby a camera is not displayed during a certain period of time on adisplay screen for displaying the captured image, calculating the indexvalue indicating a degree of change between a state of a monitoringtarget presented before the period of time and a state of the monitoringtarget displayed after the period of time, and using a captured imagepresented after the period of time as the first captured image.
 31. Themonitoring system according to claim 22, wherein the at least oneprocessor is further configured to perform: when a first partial regionof a display screen for displaying the captured image is not included ina screen region corresponding to an eye gaze direction or a facedirection of a user viewing the display screen during a certain periodof time, calculating an index value indicating a degree of changebetween a state of the monitoring target presented on a first partialregion before the period of time and a state of the monitoring targetpresented in the first partial region after the period of time; andpresenting an indication based on the index value calculated for thefirst partial region on a region presented in the first partial regionin the first captured image, using the captured image presented afterthe period of time as the first captured image.
 32. An informationprocessing method executed by a computer comprising: calculating anindex value indicating a degree of change in a state of a monitoringtarget in a plurality of captured images, the captured images beingcaptured at different times; and presenting an indication based on theindex value on a first captured image, wherein a train is captured inthe captured images, the degree of change is calculated based on thestate of the monitoring target at times when a door of the train opensand when the door of the train closes.
 33. The information processingmethod according to claim 32, further comprising calculating the indexvalue so as to indicate the degree of change in a degree of crowdednessof a plurality of objects included in the monitoring target.
 34. Theinformation processing method according to claim 32, wherein themonitoring target includes a queue or a crowd of objects, the methodfurther comprises calculating the index value so as to indicate thedegree of change in length or speed of the queue or the crowd.
 35. Theinformation processing method according to claim 32, further comprising:determining an indication color based on the index value, with respectto the monitoring target; and changing a color of the monitoring targetor a color around the monitoring target into the indication colordetermined for the monitoring target, in the first captured image. 36.The information processing method according to claim 32, furthercomprising presenting an indication for emphasizing a monitoring targetmore as the index value of the monitoring target becomes larger, orpresenting an indication for emphasizing a monitoring target more as theindex value of the monitoring target becomes smaller.
 37. Theinformation processing method according to claim 32, further comprising:calculating a degree of divergence between the index value and areference degree of change; and presenting an indication for emphasizinga monitoring target more as the degree of divergence of the monitoringtarget becomes higher, or presenting an indication for emphasizing amonitoring target more as the degree of divergence of the monitoringtarget becomes lower in the first captured image.
 38. The informationprocessing method according to claim 37, further comprising: determiningan indication color based on the degree of divergence calculated for themonitoring target, with respect to the monitoring target; and changing acolor of the monitoring target or a color around the monitoring targetinto the indication color determined for the monitoring target, in thefirst captured image.
 39. The information processing method according toclaim 32, further comprising: calculating a predicted value of thedegree of change in the state of the monitoring target at and after atime when each image used for the calculation is captured, thecalculation of the predicted value being performed using the calculateddegree of change in the state of the monitoring target; and setting thepredicted value as the index value.
 40. The information processingmethod according to claim 32, further comprising: when an image capturedby a camera is not displayed during a certain period of time on adisplay screen for displaying the captured image, calculating the indexvalue indicating a degree of change between a state of a monitoringtarget presented before the period of time and a state of the monitoringtarget displayed after the period of time, and using a captured imagepresented after the period of time as the first captured image.
 41. Anon-transitory computer readable storage medium storing a program thatcauses a computer to execute the image processing method according toclaim 32.